Advancing Topical Text Classification: A Novel Distance-Based Method with Contextual Embeddings
Andriy Kosar, Guy De Pauw, Textgain, Antwerp, Belgium, Walter M. P. Daelemans · 2023
This study introduces a new method for distance-based unsupervised topical text classification using contextual embeddings.The method applies and tailors sentence embeddings for distance-based topical text classification.This is achieved by leveraging the semantic similarity between topic labels and text content, and reinforcing the relationship between them in a shared semantic space.The proposed method outperforms a wide range of existing sentence embeddings on average by 35%.Presenting an alternative to the commonly used transformer-based zero-shot general-purpose classifiers for multiclass text classification, the method demonstrates significant advantages in terms of computational efficiency and flexibility, while maintaining comparable or improved classification results.